Building with LLMs and RAG: From Concepts to Implementation

 Building with LLMs and RAG: From Concepts to Implementation" provides participants with the conceptual foundation and technical know-how to apply Large Language Models effectively through Retrieval-Augmented Generation techniques. This intensive 4-day course is designed for technical professionals, data scientists, ML engineers, and technical leaders who want to understand how LLMs work, how retrieval pipelines enhance accuracy and factual grounding, and how to deploy such systems responsibly in production environments.

The course bridges the gap between theory and practice, helping learners design, build, and evaluate RAG architectures for real-world use cases. Through a combination of conceptual lectures, technical deep-dives, and hands-on implementation exercises, participants will gain practical experience building three progressively sophisticated systems: a basic LLM chatbot, a functional RAG pipeline, and an optimized production-ready RAG application.

• Foundation and First Implementation: Participants begin with a thorough understanding of Large Language Model fundamentals, including transformer architecture, training processes, tokenization, and embeddings. You'll explore how LLMs generate text through sampling strategies and learn prompt engineering techniques to optimize responses. The second part of this session focuses on practical implementation: connecting to LLM APIs, working with open-source models, and building your first functional chatbot with optimized latency and response quality.

• Retrieval-Augmented Generation: The second part of the course addresses the critical limitations of standalone LLMs, hallucinations, knowledge cutoff dates, and lack of domain-specific knowledge, and introduces RAG as the solution. Participants will master information retrieval fundamentals including keyword search (TF-IDF, BM25), semantic search with embeddings, hybrid search techniques, and metadata filtering. You'll learn to work with vector databases (FAISS, Pinecone, Chroma, Weaviate) and implement comprehensive evaluation strategies. The hands-on session guides you through building a complete RAG system, from knowledge base construction to chatbot integration.

• Advanced Techniques and Production Deployment: The final part explores advanced optimization techniques that separate prototype systems from production-ready applications. Topics include approximate nearest neighbors (ANN) algorithms for scaling, advanced chunking strategies, query parsing and rewriting, cross-encoders, and reranking methods. Participants will learn about agentic RAG systems that can use tools and make autonomous decisions, multimodal RAG for processing images and documents, and the strategic choice between RAG and fine-tuning approaches. Critical production considerations are covered in depth: quantization for deployment, logging and monitoring, performance vs. cost tradeoffs, security measures, and bias mitigation.

• First part: Understanding LLMs
• • What is AI and the evolution to LLMs
  • Deep dive into Transformer architecture
  • LLM training processes and capabilities
  • Tokenization, embeddings, and contextual understanding
  • Sampling strategies and quantization
  • Prompt Engineering fundamentals

• Second part: Construct Your First Chatbot
• • Using LLMs through APIs (setup, cost considerations, key parameters)
  • Using open-source LLMs (setup, quantization, serving)
  • Connect the model to a chat interface
  • Optimize latency and response quality
  • Hands-on: Build a basic chatbot

• Third part: Basics of RAG
• • Limitations of LLMs and the motivation for RAG
  • RAG Architecture overview (retriever, vector DB, generator)
  • Vector store basics and embedding models
  • Information Retrieval Fundamentals:
  • • Keyword search: TF-IDF and BM25
    • Semantic search principles
    • Hybrid Search (combining methods)
    • Metadata filtering
  • RAG evaluation strategies (precision, recall, F1)
  • Security considerations
  • Handling hallucinations

• Fourth part: Implementing a Small RAG
• • Choosing the right tools (vector stores: FAISS, Pinecone, Chroma, Weaviate)
  • Constructing and organizing your first knowledge base
  • Add RAG to your initial chatbot
  • Evaluate the first prototype
  • Hands-on: Build end-to-end RAG system

• Fifth part: Advanced RAG
• • Vector Database Deep Dive:
  • • Approximate Nearest Neighbors (ANN) algorithms
    • Scaling considerations
  • Optimization Strategies:
  • • Chunking strategies and advanced techniques
    • Query parsing and rewriting
    • Cross-encoders and ColBERT
    • Reranking techniques
  • Advanced Prompt Engineering:
  • • Building effective augmented prompts
    • Few-shot learning techniques
    • Chain-of-thought prompting
  • Agentic RAG:
  • • Tool integration (Tools and MCP)
    • Autonomous decision-making
  • Customized Evaluation:
  • • Component-level testing
    • End-to-end evaluation
    • Custom metrics

• • Cost/Latency vs Response Quality tradeoffs

• Sixth part: Production RAG
• • Multimodal RAG (text, images, documents)
  • Production Considerations:
  • • Logging, monitoring, and observability
    • Quantization for deployment
    • Security and data privacy
    • Bias mitigation
  • RAG vs Fine-tuning: When to use each approach
  • Hands-on: Improve and productionize the RAG chatbot

• Understand the principles and architecture of Large Language Models (LLMs) and RAG systems

• Implement a basic RAG pipeline integrating an LLM and a retrieval mechanism

• Evaluate and fine-tune RAG performance for factual accuracy and user intent

• Identify opportunities for responsible adoption of RAG solutions within organizations

• Explain business and ethical implications of AI-driven retrieval systems

This course combines theoretical instruction with hands-on practical exercises, demonstrations, and discussions. Participants will engage in small project work to build and test RAG-enabled applications using open-source tools.